Brain functional network integrity sustains cognitive ...€¦ · 22/11/2019 · 8 Tagliavini,20...

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1

Brain functional network integrity sustains cognitive function despite 2

atrophy in presymptomatic genetic frontotemporal dementia. 3

4

Kamen A. Tsvetanov,*,1,2 Stefano Gazzina,*,1,3 Simon P. Jones,1 John van Swieten,3 Barbara Borroni,4 Raquel Sanchez-5

Valle,5 Fermin Moreno,6, 7 Robert Laforce Jr,8 Caroline Graff,9 Matthis Synofzik,10,11 Daniela Galimberti,12,13 Mario 6

Masellis,14 Maria Carmela Tartaglia,15 Elizabeth Finger,16 Rik Vandenberghe,17,18 Alexandre de Mendonça,19 Fabrizio 7

Tagliavini,20 Isabel Santana,21,22,23 Simon Ducharme,24,25 Chris Butler,26 Alex Gerhard,27 Adrian Danek,28 Johannes 8

Levin,28 Markus Otto,29 Giovanni Frisoni,30,31 Roberta Ghidoni,32 Sandro Sorbi,33,34 Jonathan D. Rohrer,35 and James B. 9

Rowe,1,2,36 on behalf of the Genetic FTD Initiative, GENFI.# 10

*Joint first authorship 11

12

1 Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK 13

2 Cambridge Centre for Ageing and Neuroscience (Cam-CAN), University of Cambridge and MRC Cognition and Brain 14

Sciences Unit, Cambridge, UK 15

3 Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands 16

4 Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental Sciences, University 17

of Brescia, Brescia, Italy 18

5 Alzheimer’s disease and Other Cognitive Disorders Unit, Neurology Service, Hospital Clínic, Institut d’Investigacións 19

Biomèdiques August Pi I Sunyer, University of Barcelona, Barcelona, Spain 20

6 Cognitive Disorders Unit, Department of Neurology, Hospital Universitario Donostia, San Sebastian, Gipuzkoa, Spain 21

7 Neuroscience Area, Biodonostia Health Research Insitute, San Sebastian, Gipuzkoa, Spain 22

8 Clinique Interdisciplinaire de Mémoire, Département des Sciences Neurologiques, CHU de Québec, and Faculté de 23

Médecine, Université Laval, Québec, Canada 24

9 Karolinska Institutet, Department NVS, Center for Alzheimer Research, Division of Neurogenetics, Stockholm, Sweden 25

10 Department of Neurodegenerative Diseases, Hertie-Institute for Clinical Brain Research & Center of Neurology, 26

University of Tübingen, Germany 27

11 German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany 28

. CC-BY-NC-ND 4.0 International licenseIt is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. was not certified by peer review)

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12 University of Milan, Centro Dino Ferrari, Milan, Italy 29

13 Fondazione IRCSS Ca’ Granda, Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, Milan, Italy 30

14 LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, Toronto, Ontario, Canada 31

15 Toronto Western Hospital, Tanz Centre for Research in Neurodegenerative Disease, Toronto, Ontario, Canada 32

16 Department of Clinical Neurological Sciences, University of Western Ontario, London, ON, Canada 33

17 Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium 34

18 Neurology Service, University Hospitals Leuven, Belgium, Laboratory for Neurobiology, VIB-KU 35

19 Laboratory of Neurosciences, Institute of Molecular Medicine, Faculty of Medicine, University of Lisbon, Lisbon, 36

Portugal 37

20 Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milan, Italy 38

21 Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal 39

22 Faculty of Medicine, University of Coimbra, Coimbra, Portugal 40

23 Centre of Neurosciences and Cell biology, Universidade de Coimbra, Coimbra, Portugal 41

24 Department of Psychiatry, McGill University Health Centre, McGill University, Montreal, Canada 42

25 McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Canada 43

26 Nuffield Department of Clinical Neurosciences, Medical Sciences Division, University of Oxford, Oxford, UK 44

27 Faculty of Medical and Human Sciences, Institute of Brain, Behaviour and Mental Health, The University of Manchester, 45

Withington, Manchester, UK 46

28 Neurologische Klinik und Poliklinik, Ludwig-Maximilians-Universität, Munich, German Center for Neurodegenerative 47

Diseases (DZNE), Munich, Germany 48

29 Department of Neurology, University Hospital Ulm, Ulm, Germany 49

30 Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, 50

Italy 51

31 Memory Clinic and LANVIE-Laboratory of Neuroimaging of Aging, University Hospitals and University of Geneva, 52

Geneva, Switzerland 53

32 Molecular Markers Laboratory, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy 54



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33 Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy 55

34 Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) “Don Gnocchi”, Florence, Italy 56

35 Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, 57

London, UK 58

36 Medical Research Council Cognition and Brain Sciences Unit, Cambridge, UK 59

60

# See Appendix for a list of GENFI consortium members. 61

62

Correspondence to: 63

Kamen A. Tsvetanov 64

e-mail: [email protected] 65

66

Keywords (up to five): frontotemporal dementia (FTD), presymptomatic, functional magnetic resonance imaging

(fMRI), network connectivity.



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Abstract 67

INTRODUCTION: The presymptomatic phase of neurodegenerative disease can last many years, with 68

sustained cognitive function despite progressive atrophy. We investigate this phenomenon in familial 69

Frontotemporal dementia (FTD). 70

METHODS: We studied 121 presymptomatic FTD mutation carriers and 134 family members without 71

mutations, using multivariate data-driven approach to link cognitive performance with both structural and 72

functional magnetic resonance imaging. Atrophy and brain network connectivity were compared between 73

groups, in relation to the time from expected symptom onset. 74

RESULTS: There were group differences in brain structure and function, in the absence of differences in 75

cognitive performance. Specifically, we identified behaviourally-relevant structural and functional network 76

differences. Structure-function relationships were similar in both groups, but coupling between functional 77

connectivity and cognition was stronger for carriers than for non-carriers, and increased with proximity to 78

the expected onset of disease. 79

DISCUSSION: Our findings suggest that maintenance of functional network connectivity enables carriers to 80

maintain cognitive performance. 81



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82

1. Introduction 83

Across the adult healthy lifespan, the structural and functional properties of brain networks are coupled, 84

and both are predictive of cognitive ability [1,2]. The connections between structure, function and 85

performance have been influential in developing current models of ageing and neurodegeneration [3–5]. 86

However, this work contrasts with the emerging evidence of neuropathological and structural changes 87

many years before the onset of symptoms of Alzheimer’s disease and frontotemporal dementia (FTD) [6–88

8]. Genetic FTD with highly-penetrant gene mutations provides the opportunity to examine the precursors 89

of symptomatic disease. Three main genes account for 10-20% of FTD cases: chromosome 9 open reading 90

frame 72 (C9orf72), granulin (GRN) and microtubule-associated protein tau (MAPT). These genes vary in 91

their phenotypic expression and in the age of onset [9]. Despite pleiotropy [10], environmental and 92

secondary genetic moderation [11,12] all three mutations cause significant structural brain changes in key 93

regions over a decade before the expected age of disease onset [7,13], confirmed by longitudinal studies 94

[14,15]. 95

The divergence between early structural change and late cognitive decline begs the question: how do 96

presymptomatic gene carriers stay so well in the face of progressive atrophy? We propose that the answer 97

lies in the maintenance of network dynamics and functional organisation [16]. Across the lifespan, 98

functional brain network connectivity predicts cognitive status [17], and this connectivity-cognition 99

relationship becomes stronger with age [18,19]. 100

Our overarching hypothesis is that for those at genetic risk of dementia, the maintenance of network 101

connectivity prevents the manifestation of symptoms despite progressive structural changes. A challenge 102

is that neither the anatomical and functional substrates of cognition nor the targets of neurodegenerative 103

disease are mediated by single brain regions: they are distributed across multi-level and interactive 104

networks. We therefore used a multivariate data-driven approach to identify differences in the 105

multidimensional brain-behaviour relationship between presymptomatic carriers and non-carriers of 106

mutations in FTD genes. We identified key brain networks [20] from a large independent population-based 107

age-matched dataset [21]. 108

We tested three key hypotheses: (i) presymptomatic carriers differ from non-carriers in brain structure and 109

brain function, but not in cognitive function, (ii) brain structure and function correlate with performance in 110



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both groups, but functional network indices are stronger predictors of cognition in carriers, and (iii) the 111

dependence on network integrity for maintaining cognitive functioning increases as carriers approach the 112

onset of symptoms. 113

2. Methods 114

2.1. Participants 115

Thirteen research sites across Europe and Canada recruited participants as part of an international 116

multicentre partnership, the Genetic Frontotemporal Initiative (GENFI). 313 participants had usable 117

structural and resting state functional magnetic resonance imaging data (MRI) [7,13]. The study was 118

approved by the institutional review boards for each site, and participants providing written informed 119

consent. Five participants were excluded due to excessive head motion (see below), resulting in 308 120

datasets for further analysis. 121

Participants were genotyped based on whether they carried a pathogenic mutation in MAPT, GRN and 122

C9orf72. In this study, we focus on non-carriers (NC, N=134) and presymptomatic carriers (PSC, N=121), i.e. 123

gene carriers with normal cognitive function based on a neurocognitive assessment (see below). 124

Participants and site investigators were blinded to the research genotyping, although a minority of 125

participants had undergone predictive testing outwith the GENFI study. See Table 1 for demographic 126

information of both groups. In keeping with other GENFI reports, the years to expected onset (EYO) were 127

calculated as the difference between age at assessment and mean age at onset within the family [7]. 128

129

2.2. Neurocognitive assessment 130

Each participant completed a standard clinical assessment consisting of medical history, family history, 131

functional status and physical examination, in complement with collateral history from a family member or 132

a close friend. In the current study 13 behavioural measures of cognitive function were correlated with 133

neuroimaging measures. These included the Uniform Data Set [22]: the Logical Memory subtest of the 134

Wechsler Memory Scale-Revised with Immediate and Delayed Recall scores, Digit Span forwards and 135

backwards from the Wechsler Memory Scale-Revised, a Digit Symbol Task, Parts A and B of the Trail Making 136

Test, the short version of the Boston Naming Test, and Category Fluency (animals). Additional tests included 137

three subscores of the Letter Fluency and Wechsler Abbreviated Scale of Intelligence Block Design task, 138

and the Mini-Mental State Examination. Latency measures for the Trail Making Test were inverted so that 139

higher values across all tests reflect better performance. 140



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141

2.3. Neuroimaging assessment 142

Figure 1 provides a schematic representation of imaging data processing pipeline and the analysis strategy 143

for linking brain-behaviour data. MRI data were acquired using 3T scanners and 1.5T where no 3T scanning 144

was available from various vendors, with optimised scanning protocols to maximise synchronisation across 145

scanners and sites [7,13]. A 3D-structural MRI was acquired on each participant using T1-weighted 146

Magnetic Prepared Rapid Gradient Echo sequence. The co-registered T1 images were segmented to extract 147

probabilistic maps of 6 tissue classes: grey matter (GM), white matter (WM), cerebrospinal fluid (CSF), 148

bone, soft tissue, and residual noise. The native-space GM and WM images were submitted to 149

diffeomorphic registration to create equally represented gene-group template images [DARTEL; 23]. The 150

templates for all tissue types were normalised to the Montreal Neurological Institute template using a 12-151

parameter affine transformation. The normalised images were smoothed using an 8-mm Gaussian kernel. 152

For resting state fMRI measurements, Echo-Planar Imaging data were acquired with at least six minutes of 153

scanning. The imaging data were analysed using Automatic Analysis [AA 4.0; ,24] pipelines and modules 154

which called relevant functions from SPM12 [25]. To quantify the total motion for each participant, the root 155

mean square volume-to-volume displacement was computed using the approach of Jenkinson et al [26]. 156

Participants with 3.5 or more standard deviations above the group mean motion displacement were 157

excluded from further analysis (N = 5). To further ensure that potential group bias in head motion did not 158

affect later analysis of connectivity, we took three further steps: i) fMRI data was further postprocessed 159

using whole-brain Independent Component Analysis (ICA) of single subject time-series denoising, with 160

noise components selected and removed automatically using a priori heuristics using the ICA-based 161

algorithm [27], ii) postprocessing of network node time-series (see below) and iii) a subject-specific 162

estimate of head movement for each participant [26] included as a covariate in group-level analysis [28]. 163

2.4. Network definition 164

The location of the key cortical regions in each network was identified by spatial-ICA in an independent 165

dataset of 298 age-matched healthy individuals from a large population-based cohort [21]. Full details 166

about preprocessing and node definition are described previously [29]. Four networks commonly affected 167

by neurodegenerative diseases including FTD [20] were identified by spatially matching to pre-existing 168

templates [30]. The node time-series were defined as the first principal component resulting from the 169



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singular value decomposition of voxels in an 8-mm radius sphere, which was centred on the peak voxel for 170

each node [18]. Visual representation of the spatial distribution of the nodes is shown in Figure 2. 171

We aimed to further reduce the effects of noise confounds on functional connectivity effects of node time-172

series using general linear model (GLM) [28]. This model included linear trends, expansions of realignment 173

parameters, as well as average signal in WM and CSF, including their derivative and quadratic regressors 174

from the time-courses of each node. The WM and CSF signals were created by using the average signal 175

across all voxels with corresponding tissue probability larger than 0.7 in associated tissue probability maps 176

available in SPM12. A band-pass filter (0.0078-0.1 Hz) was implemented by including a discrete cosine 177

transform set in the GLM. Finally, the functional connectivity (FC) between each pair of nodes was 178

computed using Pearson’s correlation on postprocessed time-series. 179

180

2.5. Statistical analysis 181

2.5.1. Group differences in brain structure, function and cognition 182

To assess the group-differences in neuroimaging and behavioural dataset we used multiple linear 183

regression with a well-conditioned shrinkage regularization [31,32] and 10-Fold Cross–Validation [33]. In 184

the analysis of brain structure we used as independent variables the mean grey matter volume (GMV) of 185

246 brain nodes [34]. In the analysis of brain function, we used the functional connectivity between 15 186

nodes, which were part of the four large-scale functional networks described above. In the analysis of 187

cognitive function, the independent variables comprised the performance measures on the 13 188

neuropsychological tests performed outside of the scanner. In all three analyses the dependent variable 189

was the genetic status (PSC vs NC) including age as a covariate of no interest. In addition, in the analysis of 190

neuroimaging data we included scanner site and head motion as additional covariates of no interest. 191

2.5.2. Brain-behaviour relationships 192

For the brain-behaviour analysis, we adopted a two-level procedure. In the first-level analysis, we assessed 193

the multidimensional brain-behaviour relationships using partial least squares [35]. This analysis described 194

the linear relationships between the two multivariate datasets, namely neuroimaging (either GMV or FC) 195

and behavioural performance, by providing pairs of latent variables (Brain-LVs and Cognition-LVs) as linear 196

combinations of the original variables which are optimised to maximise their covariance. Namely, dataset 197

1 consisted of a brain feature set, which could be either grey matter volume (GMV dataset) or functional 198



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connectivity strength between pairs of regions for each individual (FC dataset). Dataset 2 included the 199

performance measures on the 13 tests (i.e. Cognition dataset), as considered in the multiple linear 200

regression analysis of group differences in cognition. Covariates of no interest included head motion, 201

scanner site, gender and handedness. 202

Next, we tested whether the identified behaviourally-relevant LVs of brain structure and function were 203

differentially expressed by NC and PSC as a function of expected years to onset. To this end, we performed 204

a second-level analysis using multiple linear regression with robust fitting algorithm as implemented in 205

matlab’s function “fitlm.m”. Independent variables included subjects’ brain scores from first level PLS 206

(either Structure-LV or Function-LV subject scores), group information, expected years to onset and their 207

interaction terms (e.g. brain scores x group, brain scores x years to expected onset, etc.). The dependent 208

variable was subjects’ cognitive scores from the first level analysis in the corresponding PLS (Cognition-LV). 209

Covariates of no interest included gender, handedness, head movement and education (Figure 1). 210

3. Results 211

3.1. Group differences in neuroimaging and cognitive data 212

Brain structure 213

The multiple linear regression model testing for overall group differences in grey matter volume between 214

PSC and NC was significant (r=.14, p=.025), reflecting expected presymptomatic differences in brain-wide 215

atrophy. The frontal, parietal and subcortical regions had most atrophy in PSC (Figure 3). As expected, the 216

group difference in grey matter volume of these regions increased as EYO decreased, see Supplementary 217

Materials. 218

Brain Function 219

The multiple linear regression model testing for overall group differences in functional connectivity 220

between PSC and NC was marginally significant (r=.12, p=.049). The pattern of connectivity indicated mainly 221

increased connectivity between SN-DMN and SN-FPN in presymptomatic carriers, coupled with decreased 222

connectivity within the networks and DMN-FPN connectivity (Figure 3). 223

Cognitive Function 224

We did not identify group differences in cognition and behaviour (r=.002, p=.807), confirming the 225

impression of “healthy” status among presymptomatic carriers. However, in the next section, we consider 226



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the relationships between structure, function and cognition that underlie this maintenance of cognitive 227

function. 228

3.2. Brain-behaviour relationships 229

Structure-cognition 230

Partial least squares analysis of grey matter volume and cognition identified one significant pair of latent 231

variables (r = .40, p = .009). This volumetric latent variable expressed negative loadings in frontal (superior 232

frontal gyrus, precentral gyrus, paracentral lobule), parietal (postcentral gyrus, precuneus, superior and 233

inferior parietal lobule) and occipital (lateral and medial occipital cortex) regions and positive loadings in 234

parahippocampal and hippocampal regions (Figure 4). The Cognition-LV profile expressed positively a large 235

array of cognitive tests, with strongest values on delayed memory, Trail Making, Digit Symbol, Boston 236

Naming and Fluency tests. The positive correlation between volumetric and cognitive LV’s confirms the 237

expected relationship across the cohort as a whole, between cortical grey matter volume and both 238

executive, language and mnemonic function (Figure 4). 239

To understand the structure-cognition relationship in each group and in relation to the expected 240

years of onset, we performed a second-level moderation analysis using a regression model: we entered 241

Cognition-LV subject scores as dependent variable, and grey matter volume LV subject scores, genetic 242

status (i.e. carrier or non-carrier), expected years to onset and their interactions as independent variables 243

in addition to covariates of no interest. The results indicated that the relationship between grey matter 244

volume and cognition could not be explained by genetic status, expected years to onset or their interactions 245

with grey matter volume LV subject scores. There was no evidence for genetic status- and onset-dependent 246

differences (over and above ageing and other covariates) in the associations between grey matter volume 247

and cognition in this analysis (Figure 4). 248

Connectivity-Cognition 249

PLS analysis of functional connectivity and cognition also identified one significant pair of LVs (Function-LV 250

and Cognition-LV, r=.32, p=.020), see Figure 5. This Function-LV reflected weak between-network 251

connectivity, coupled with strong within-network connectivity. This pattern indicates the segregation or 252

modularity of large-scale brain networks. The Cognition-LV expressed all tests, with positive loading values 253

indicating that higher performance on a wide range of cognitive tests is associated with stronger functional 254

network segregation. Cognitive deficits were associated with loss of segregation, with increased between-255

network connectivity and decreased within-network connectivity. 256



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To further test whether the observed behaviourally-relevant pattern of connectivity is differentially 257

expressed between genetic status groups and expected years of onset, we constructed a second-level 258

regression model with robust error estimates by including Function-LV subject scores, genetic status, 259

expected years of onset and their interaction terms as independent variables and Cognition-LV as 260

dependent variable in addition to covariates of no interest (Figure 5). 261

We found evidence for significant interaction between expected years of onset and Function-LV (r=.21, 262

p<.001) and between group and Function-LV (r=.15, p=.003) explaining unique variance in Cognition-LV. 263

We used the Dawson and Richter approach [36] to test formally for differences in the relationship (i.e. 264

simple slopes) between Function-LV and Cognition-LV for PSC and NC. The relationship between Function-265

LV and Cognition-LV was stronger for PSC relative to NC (r=.15, p=.003), indicating the increasing 266

importance of functional connectivity between the large-scale networks for PSC participants to maintain 267

performance (Figure 5). 268

For ease of interpretation and illustration, we also computed the correlation between Cognition-LV and 269

Function-LV for high and low levels of expected years to onset (EYO) within each group separately, where 270

the levels were taken to be 1 standard deviation above and below the mean values of EYO. The two EYO 271

subgroups were labelled “near” and “far”, with “near” for EYO values close to zero (i.e. participant’s age is 272

“near” the age at which disease symptoms were demonstrated in the family), and “far” for EYO being a 273

largely negative value (i.e. participant’s age is “far” from the age at which disease symptoms were 274

demonstrated in the family). The analysis indicated that as the EYO decreases (i.e. participant’s age is 275

reaching the years of onset of symptoms) the relationship between functional connectivity and 276

performance becomes stronger (moderation effects: r=.14, p=.021 and r=.28, p<.001 for NC and PSC, 277

respectively). Interestingly this relationship was stronger in each EYO subgroup for PSC relative to NC (PSC-278

Near vs NC-Near: r=.28, p<.001; PSC-Far vs NC-Far: r=.22, p=.001). These findings indicate that the 279

relationship between FC and cognition is stronger in PSC relative to NC, and that this relationship increases 280

as a function of EYO. 281

282

4. Discussion 283

In the present study, we confirmed previous findings of group differences in brain structure and function, 284

in the absence of differences in cognitive performance between non-carriers and presymptomatic carriers 285



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of FTD-related genetic mutations. But, while the relationship between structure and cognition was similar 286

in both groups, the coupling between function and cognition was stronger for presymptomatic carriers, 287

and increased as they approached the expected onset of disease. 288

These results suggest that people can maintain good cognitive abilities and successful day-to-day 289

functioning despite significant neuronal loss and atrophy. This disjunction between structure and function 290

is a feature of healthy ageing, but we have shown that it also characterises presymptomatic FTD, over and 291

above the age effects in their other family members, despite widespread progressive atrophy. The 292

multivariate approach reveals two key findings: (i) presymptomatic carriers express stronger between-293

network and weaker within-network functional connectivity than age-matched non-carriers, and (ii) as 294

carriers approach their estimated age of symptom onset, and atrophy becomes evident, the maintenance 295

of good cognition is increasingly associated with sustaining balance of within- and between-network 296

integration. 297

This balance of within- and between-network connectivity is characteristic of segregated and specialized 298

network organization of brain systems. Such functional segregation varies with physiological ageing 299

[17,18,37], with cognitive function [18] and in individuals at risk for Alzheimer’s disease [38]. Graph-300

theoretic quantification of network organisation confirms the relevance of modularity and efficiency to 301

function in FTD [16]. Conversely, the loss of neural systems’ modularity mirrors the loss of functional 302

specialization with age [39] and dementia [38]. Here, we show the significance of the maintenance of this 303

functional network organisation, with a progressively stronger correlation with cognitive performance as 304

seemingly healthy adults approach the age of expected onset of FTD. 305

The uncoupling of brain function from brain structure indicates that there may be independent and 306

synergistic effects of multiple factors leading to cognitive preservation. This is consistent with a previous 307

work in healthy ageing where brain activity and connectivity provide independent and synergistic 308

predictions of performance across the lifespan [19]. Therefore, future studies need to consider the 309

independent and synergistic effects of many possible biomarkers, based on MRI, computed tomography, 310

positron-emission tomography, CSF, blood and brain histopathology. For example, functional network 311

impairment may be related to tau expression and tau pathology, amyloid load, or neurotransmitter deficits 312

in neurodegenerative diseases, independent of atrophy [29,40–42]. Importantly, studies need to recognise 313

the rich multivariate nature of cognition and of neuroimaging in order to improve stratification procedures, 314

e.g. based on integrative approaches that explain individual differences in cognitive impairment [29,43]. 315



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We also recognise the difficulty to determine a unique contribution of each factor (e.g. brain structure and 316

brain function), given the increasing interaction between factors in advanced stages of disease [44]. This is 317

further complicated by these alterations becoming irreversible with progression of neurodegeneration 318

[45]. This suggests that the critical interplay between multiple factors (including brain structure and 319

function) may be better studied in the asymptomatic and preclinical stages as well as across the healthy 320

lifespan, which could still be modifiable and their influences are likely to be more separable. 321

Our findings agree with the model of compensation in the presymptomatic and early phases of 322

Huntington’s disease, where network coupling predicted better cognitive performance [46]. In a recent 323

longitudinal study a non-linear concave-down pattern of both brain activity and behaviour was present, 324

despite a linear decline in brain volume over time, [47]. Similar effects have been observed also in healthy 325

ageing and amnestic mild cognitive impairment, where greater connectivity with the default-mode network 326

and weaker connectivity between default-mode network and dorsal-attention network was associated with 327

higher cognitive status in both groups [48]. Network integrity may also play a role in compensatory 328

mechanisms in non-cognitive symptoms, such as motor impairment in Parkinson’s disease [49]. 329

Accordingly, increased network efficiency and connectivity has been shown in prodromal phases, followed 330

by decreased local connectivity in symptomatic phases, suggesting the emergence and dissipation of neural 331

compensation [50]. 332

The current study has several limitations. First, despite the large sample of subjects included, we did not 333

analyse each gene separately, as it may have resulted in too small and unbalanced samples, lowering 334

statistical power and robustness. Moreover, genetic FTD is also characterised by multiple mutations within 335

these genes and pleiotropy of clinical phenotypes even for the same mutation [10]. Pleiotropy in terms of 336

clinical phenotype is avoided by the study of presymptomatic carriers, but we cannot rule out pleiotropy 337

of intermediate phenotypes expressed as say neural network diversity. Furthermore, clinical heterogeneity 338

is modified by environmental factors such as education [which may be a surrogate of cognitive reserve, 51], 339

in FTD as in other dementias [12]. Genetic modifiers such as TMEM106B [52], APOE [53], have also been 340

identified. Further work, with larger cohorts, will be able to test the potential effect of these moderators 341

on the relationships between brain structure, functional networks and cognition. Future studies will benefit 342

from using brain measures that reflect differences in neural connectivity directly from neurophysiology or 343

separation of neurovascular from neuronal contributors to BOLD fMRI variance [18,54], which can 344

confound the effects of age, drug or disease [55]. 345



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The current study is cross-sectional. Therefore, we cannot infer longitudinal progression within subjects as 346

the unambiguous cause of the effects we observe in relation to expected years of onset. Accumulating 347

evidence suggests that network integrity serves to maintain performance with either physiological ageing 348

or pathological conditions. However, longitudinal mediation studies and pharmacological or electroceutical 349

interventions would be needed to prove its causal role in cognitive preservation. Finally, our findings are 350

limited to autosomal dominant FTD, which represents a minority of FTD: generalisation to sporadic forms 351

of disease would be speculative. 352

In conclusion, we used a multivariate data-driven approach to demonstrate that brain functional integrity 353

can enable presymptomatic carriers to maintain cognitive performance in the presence of progressive brain 354

atrophy for years before the onset of symptoms. The approach and the findings have implications for the 355

design of presymptomatic disease-modifying therapy trials and the study of unique and synergistic effects 356

of risk factors and biomarkers in health and disease, which are otherwise increasingly interacting and 357

irreversible with progression of neurodegeneration. 358

359



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360

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517

518

519



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520

6. Acknowledgements 521

Kamen A. Tsvetanov is supported by the British Academy (Postdoctoral Fellowship, PF160048). 522

James B Rowe is supported by the Wellcome Trust (103838) the Medical Research Council (SUAG/051 523

G101400) and the Cambridge NIHR Biomedical Research Centre. Raquel Sánchez-Valle is supported by the 524

Instituto de Salud Carlos III and the JPND network PreFrontAls (01ED1512/AC14/0013) and the Fundació 525

Marató de TV3 (20143810). 526

527



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528

7. Tables 529

Table 1. Demographics of participants included in the analysis, grouped by genetic status 530

as non-carriers (NC) and presymptomatic carriers (PSC). * denotes whether demographics vary 531

between NC and PSC groups. 532

533

NC PSC X2 or F-test P -value

N 134 121

2.68 0.103

Mean / SD 49 / 14 46 / 11

Range [Min/Max] 19 / 86 20 / 70

0.01 0.908

Men 53 (39.6) 47 (38.8)

Women 81 (60.4) 74 (61.2)

0.23 0.631

Mean / SD -10 / 12 -11 / 11

Range [Min/Max] -25 / 10 -25 / 10

0.05 0.826

Mean / SD 14 / 3 14 / 3

Range [Min/Max] 5 / 24 5 / 22

0.39 0.532

Mean / SD 29 / 1 29 / 1

Range [Min/Max] 25 / 30 23 / 30

Education

Gene Carrier Group Statistical tests*

Age

Gender, n (%)

Expected Years to Onset

Mini-Mental State Examination



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8. Figures 534

535

536

Figure 1. Schematic representation of data processing and analysis pipeline to test for 537

brain-behaviour differences between presymptomatic carriers (PSC) and non-carriers (NC) as a 538

function of expected years to onset (EYO) of symptoms, while controlling for covariates of no 539

interest (Covs). Brain structural measures were based on the mean grey matter volume (GMV) in 540

246 nodes, as defined in the Brainnetome atlas [34]. Brain functional measures were based on the 541

functional connectivity between 15 nodes as part of four large-scale networks, which were defined 542

in an independent cohort of 298 age-matched individuals part of the Cam-CAN dataset 543

(Passamonti et al 2019 BioArxiv). 544

Leve

l 1

Partial Least Squares (PLS)

Leve

l 2

Cognitive Function- 10 behavioural tests

- 13 measures

Latent VariableBrain

Latent Variable Cognition

Test1

TestN

Node1

NodeN

Multiple Linear Regression (MLR)

Brain Structure- T1-weighted MRI- Processing- Extract GMV for 246

nodes (Brainnetome Atlas)

Brain Function- Process resting-state fMRI- Define networks & nodes (Cam-CAN cohort)- Extract timeseries in four networks (15 nodes)- Estimate functional connectivity

NC (N=134)PSCs (N=121)



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545

Figure 2. Visualisation of spatial localisation of the nodes part of the four large-scale 546

networks and their mean functional connectivity (circular plot) across all participants in this study. 547

Nodes and networks were defined in an independent cohort of 298 age-matched individuals part 548

of the Cam-CAN dataset (Passamonti et al 2019).The default mode network (DMN) contained five 549

nodes: the ventral anterior cingulate cortex (vACC), dorsal and ventral posterior cingulate cortex 550

(vPCC and dPCC), and right and left inferior parietal lobes (rIPL and lIPL). The salience network 551

(SN) was defined using right and left anterior insular (rAI and lAI) and dorsal anterior cingulate 552

cortex (dACC). The frontoparietal network (FPN) was defined using right and left anterior superior 553

frontal gyrus (raSFG and laSFG), and right and left angular gyrus (rAG and lAG). The dorsal 554

attention Network (DAN) was defined using right and left intraparietal sulcus (rIPS and lIPS). 555

556

557

558



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559

Figure 3. Group differences between PSC and NC in grey matter volume (left panel) and 560

functional connectivity between nodes within four large scale networks (right panel). Hot colour 561

scheme indicates the strength of effect size of PSC showing higher GMV and FC than NC, while cold 562

colour scheme indicates the opposite effect (i.e. NC > PSC). 563



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564

Figure 4. PLS analysis of grey matter volume (GMV) and cognition indicating the spatial 565

distribution of GMV loading values (a), where hot and cold colour schemes are used for the strength 566

of positive and negative correlations with the profile of Cognitive LV (b). (c) The scatter plot on the 567

left represents the relationship between subjects scores of GMV LV and Cognition LV for 568

presymptomatic carriers (PSC) and non-carriers (NC). The scatter plots in the middle and right 569

hand-side represent GMV-Cognition LV relationship as a function of expected years to onset (EYO, 570

split in two groups, Near and Far, see text) in each gene group separately. 571



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572

Figure 5. PLS analysis of functional connectivity and cognition indicating the connectivity 573

pattern of loading values (a), where hot and cold colour schemes are used for the strength of 574

positive and negative correlations with the profile of Cognitive LV (b). (c) The scatter plot on the 575

left represents the relationship between subjects scores of Function LV and Cognition LV for 576

presymptomatic carriers (PSC) and non-carriers (NC). The scatter plots in the middle and right 577

hand-side represents Function-Cognition LV relationship as a function of expected years to onset 578

(EYO split in two groups, Near and Far, see text) in each gene group separately, which is also 579

represented using a bar chart in (d). * denotes significant test at p-value < 0.05. 580



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9. APPENDIX 581

List of other GENFI consortium members 582 Sónia Afonso - Instituto Ciencias Nucleares Aplicadas a Saude, Universidade de Coimbra, Coimbra, Portugal 583 Maria Rosario Almeida - Centre of Neurosciences and Cell Biology, Universidade de Coimbra, Coimbra, Portugal 584 Sarah Anderl-Straub – Department of Neurology, Ulm University, Ulm, GermanyChristin Andersson - Department of 585 Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden 586 Anna Antonell - Alzheimer’s disease and other cognitive disorders unit, Neurology Department, Hospital Clinic, 587 Institut d’Investigacions Biomèdiques, Barcelona, Spain 588 Silvana Archetti - Biotechnology Laboratory, Department of Diagnostics, Spedali Civili Hospital, Brescia, Italy 589 Andrea Arighi - Fondazione IRCSS Ca’ Granda, Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, 590 Milan, Italy 591 Mircea Balasa - Alzheimer’s disease and other cognitive disorders unit, Neurology Department, Hospital Clinic, 592 Institut d’Investigacions Biomèdiques, Barcelona, Spain 593 Myriam Barandiaran - Neuroscience Area, Biodonostia Health Research Institute, Paseo Dr Begiristain sn, CP 20014, 594 San Sebastian, Gipuzkoa, Spain 595 Nuria Bargalló - Radiology Department, Image Diagnosis Center, Hospital Clínic and Magnetic Resonance Image core 596 facility, IDIBAPS, Barcelona, Spain 597 Robart Bartha - Department of Medical Biophysics, Robarts Research Institute, University of Western Ontario, 598 London, Ontario, Canada 599 Benjamin Bender - Department of Diagnostic and Interventional Neuroradiology, University of Tuebingen, 600 Tuebingen, Germany 601 Luisa Benussi - Istituto di Ricovero e Cura a Carattere Scientifico Istituto Centro San Giovanni di Dio Fatebenefratelli, 602 Brescia, Italy 603 Valentina Bessi - Department of Neuroscience, Psychology, Drug Research, and Child Health, University of Florence, 604 Florence, Italy 605 Giuliano Binetti - Istituto di Ricovero e Cura a Carattere Scientifico Istituto Centro San Giovanni di Dio 606 Fatebenefratelli, Brescia, Italy 607 Sandra Black - LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, Toronto, Canada 608 Martina Bocchetta – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of 609 Neurology, Queen Square London, UK 610 Sergi Borrego-Ecija - Alzheimer’s disease and other cognitive disorders unit, Neurology Department, Hospital Clinic, 611 Institut d’Investigacions Biomèdiques, Barcelona, Spain 612 Jose Bras – Dementia Research Institute, Department of Neurodegenerative Disease, UCL Institute of Neurology, 613 Queen Square, London, UK 614 Rose Bruffaerts - Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium 615 Paola Caroppo - Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milan, 616 Italy 617 David Cash – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, 618 Queen Square, London, UK 619 Miguel Castelo-Branco - Neurology Department, Centro Hospitalar e Universitário de Coimbra, Instituto de Ciências 620 Nucleares Aplicadas à Saúde (ICNAS), Coimbra, Portugal 621 Rhian Convery – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, 622 Queen Square, London, UK 623 Thomas Cope – Department of Clinical Neuroscience, University of Cambridge, Cambridge, UK 624 Maura Cosseddu – Centre for Neurodegenerative Disorders, Neurology Unit, Spedali Civili Hospital, Brescia, Italy 625 María de Arriba - Neuroscience Area, Biodonostia Health Research Institute, Paseo Dr Begiristain sn, CP 20014, San 626 Sebastian, Gipuzkoa, Spain 627 Giuseppe Di Fede - Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, 628 Milan, Italy 629 Zigor Díaz - CITA Alzheimer, San Sebastian, Spain 630



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Katrina M Moore – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of 631 Neurology, Queen Square, London, UK 632 Diana Duro - Faculty of Medicine, Universidade de Coimbra, Coimbra, Portugal 633 Chiara Fenoglio - University of Milan, Centro Dino Ferrari, Milan, Italy 634 Camilla Ferrari - Department of Neuroscience, Psychology, Drug Research, and Child Health, University of Florence, 635 Florence, Italy 636 Carlos Ferreira - Instituto Ciências Nucleares Aplicadas à Saúde, Universidade de Coimbra, Coimbra, Portugal 637 Catarina B. Ferreira - Faculty of Medicine, University of Lisbon, Lisbon, Portugal 638 Toby Flanagan – Faculty of Biology, Medicine and Health, Division of Neuroscience and Experimental Psychology, 639 University of Manchester, Manchester, UK 640 Nick Fox – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, 641 Queen Square, London, UK 642 Morris Freedman - Division of Neurology, Baycrest Centre for Geriatric Care, University of Toronto, Toronto, Canada 643 Giorgio Fumagalli - Fondazione IRCSS Ca’ Granda, Ospedale Maggiore Policlinico, Neurodegenerative Diseases Unit, 644 Milan, Italy; Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, 645 Florence, Italy 646 Alazne Gabilondo - Neuroscience Area, Biodonostia Health Research Institute, Paseo Dr Begiristain sn, CP 20014, San 647 Sebastian, Gipuzkoa, Spain 648 Roberto Gasparotti - Neuroradiology Unit, University of Brescia, Brescia, Italy 649 Serge Gauthier - Department of Neurology and Neurosurgery, McGill University, Montreal, Québec, Canada 650 Stefano Gazzina - Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and Experimental 651 Sciences, University of Brescia, Brescia, Italy 652 Giorgio Giaccone - Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, 653 Milan, Italy 654 Ana Gorostidi - Neuroscience Area, Biodonostia Health Research Institute, Paseo Dr Begiristain sn, CP 20014, San 655 Sebastian, Gipuzkoa, Spain 656 Caroline Greaves – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of 657 Neurology, Queen Square London, UK 658 Rita Guerreiro – Dementia Research Institute, Department of Neurodegenerative Disease, UCL Institute of 659 Neurology, London, UK 660 Carolin Heller – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, 661 Queen Square, London, UK 662 Tobias Hoegen - Department of Neurology, Ludwig-Maximilians-University of Munich, Munich, Germany 663 Begoña Indakoetxea - Cognitive Disorders Unit, Department of Neurology, Donostia University Hospital, Paseo Dr 664 Begiristain sn, CP 20014, San Sebastian, Gipuzkoa, Spain 665 Vesna Jelic - Division of Clinical Geriatrics, Karolinska Institutet, Stockholm, Sweden 666 Lize Jiskoot - Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands 667 Hans-Otto Karnath - Section of Neuropsychology, Department of Cognitive Neurology, Center for Neurology & 668 Hertie-Institute for Clinical Brain Research, Tübingen, Germany 669 Ron Keren - University Health Network Memory Clinic, Toronto Western Hospital, Toronto, Canada 670 Maria João Leitão - Centre of Neurosciences and Cell Biology, Universidade de Coimbra, Coimbra, Portugal 671 Albert Lladó - Alzheimer’s disease and other cognitive disorders unit, Neurology Department, Hospital Clinic, Institut 672 d’Investigacions Biomèdiques, Barcelona, Spain 673 Gemma Lombardi - Department of Neuroscience, Psychology, Drug Research and Child Health, University of 674 Florence, Florence, Italy 675 Sandra Loosli - Department of Neurology, Ludwig-Maximilians-University of Munich, Munich, Germany 676 Carolina Maruta - Lisbon Faculty of Medicine, Language Research Laboratory, Lisbon, Portugal 677 Simon Mead - MRC Prion Unit, Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen 678 Square, London, UK 679 Lieke Meeter - Department of Neurology, Erasmus Medical Center, Rotterdam, Netherlands 680 Gabriel Miltenberger - Faculty of Medicine, University of Lisbon, Lisbon, Portugal 681 Rick van Minkelen - Department of Clinical Genetics, Erasmus Medical Center, Rotterdam, The Netherlands 682 Sara Mitchell - LC Campbell Cognitive Neurology Research Unit, Sunnybrook Research Institute, Toronto, Canada 683



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Benedetta Nacmias - Department of Neuroscience, Psychology, Drug Research and Child Health, University of 684 Florence, Florence, Italy 685 Mollie Neason - Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, 686 Queen Square, London, UK 687 Jennifer Nicholas – Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK 688 Linn Öijerstedt - Department of Geriatric Medicine, Karolinska Institutet, Stockholm, Sweden 689 Jaume Olives - Alzheimer’s disease and other cognitive disorders unit, Neurology Department, Hospital Clinic, Institut 690 d’Investigacions Biomèdiques, Barcelona, Spain 691 Alessandro Padovani - Centre for Neurodegenerative Disorders, Neurology Unit, Department of Clinical and 692 Experimental Sciences, University of Brescia, Brescia, Italy 693 Jessica Panman – Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands 694 Janne Papma - Department of Neurology, Erasmus Medical Center, Rotterdam, The Netherlands 695 Irene Piaceri - Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, 696 Florence 697 Michela Pievani - Istituto di Ricovero e Cura a Carattere Scientifico Istituto Centro San Giovanni di Dio 698 Fatebenefratelli, Brescia, Italy 699 Yolande Pijnenburg - VUMC, Amsterdam, The Netherlands 700 Cristina Polito - Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, Nuclear Medicine Unit, 701 University of Florence, Florence, Italy 702 Enrico Premi - Stroke Unit, Neurology Unit, Spedali Civili Hospital, Brescia, Italy 703 Sara Prioni - Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, Milan, 704 Italy 705 Catharina Prix - Department of Neurology, Ludwig-Maximilians-University Munich, Germany 706 Rosa Rademakers - Department of Neurosciences, Mayo Clinic, Jacksonville, Florida, USA 707 Veronica Redaelli - Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, 708 Milan, Italy 709 Tim Rittman – Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK 710 Ekaterina Rogaeva - Tanz Centre for Research in Neurodegenerative Diseases, University of Toronto, Toronto, 711 Canada 712 Pedro Rosa-Neto - Translational Neuroimaging Laboratory, McGill University Montreal, Québec, Canada 713 Giacomina Rossi - Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, 714 Milan, Italy 715 Martin Rossor – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, 716 Queen Square, London, UK 717 Beatriz Santiago - Neurology Department, Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal 718 Elio Scarpini - University of Milan, Centro Dino Ferrari, Milan, Italy; Fondazione IRCSS Ca’ Granda, Ospedale Maggiore 719 Policlinico, Neurodegenerative Diseases Unit, Milan, Italy 720 Sonja Schönecker - Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany 721 Elisa Semler – Department of Neurology, Ulm University, Ulm, Germany 722 Rachelle Shafei – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, 723 Queen Square, London, UK 724 Christen Shoesmith - Department of Clinical Neurological Sciences, University of Western Ontario, London, Ontario, 725 Canada 726 Miguel Tábuas-Pereira - Centre of Neurosciences and Cell Biology, Universidade de Coimbra, Coimbra, Portugal 727 Mikel Tainta - Neuroscience Area, Biodonostia Health Research Institute, Paseo Dr Begiristain sn, CP 20014, San 728 Sebastian, Gipuzkoa, Spain 729 Ricardo Taipa - Neuropathology Unit and Department of Neurology, Centro Hospitalar do Porto - Hospital de Santo 730 António, Oporto, Portugal 731 David Tang-Wai - University Health Network Memory Clinic, Toronto Western Hospital, Toronto, Canada 732 David L Thomas - Neuroradiological Academic Unit, UCL Institute of Neurology, London, UK 733 Hakan Thonberg - Center for Alzheimer Research, Division of Neurogeriatrics, Karolinska Institutet, Stockholm, 734 Sweden 735 Carolyn Timberlake - University of Cambridge, Cambridge, UK 736



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Pietro Tiraboschi - Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Istituto Neurologico Carlo Besta, 737 Milano, Italy 738 Philip Vandamme - Neurology Service, University Hospitals Leuven, Belgium; Laboratory for Neurobiology, VIB-KU 739 Leuven Centre for Brain Research, Leuven, Belgium 740 Mathieu Vandenbulcke - Geriatric Psychiatry Service, University Hospitals Leuven, Belgium; Neuropsychiatry, 741 Department of Neurosciences, KU Leuven, Leuven, Belgium 742 Michele Veldsman - University of Oxford, UK 743 Ana Verdelho - Department of Neurosciences, Santa Maria Hospital, University of Lisbon, Portugal 744 Jorge Villanua - OSATEK Unidad de Donostia, San Sebastian, Gipuzkoa, Spain 745 Jason Warren – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, 746 Queen Square, London, UK 747 Carlo Wilke - Hertie Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany 748 Ione Woollacott – Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of 749 Neurology, Queen Square, London, UK 750 Elisabeth Wlasich - Neurologische Klinik, Ludwig-Maximilians-Universität München, Munich, Germany 751 Henrik Zetterberg - Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK 752 Miren Zulaica - Neuroscience Area, Biodonostia Health Research Institute, Paseo Dr Begiristain sn, CP 20014, San 753 Sebastian, Gipuzkoa, Spain. 754 755



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